An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

被引:23
|
作者
Zhen, Rong [1 ]
Gu, Qiyong [1 ]
Shi, Ziqiang [1 ]
Suo, Yongfeng [1 ]
机构
[1] Jimei Univ, Nav Coll, Xiamen 361021, Peoples R China
基金
中国国家自然科学基金;
关键词
ship global path planning; A-star algorithm; navigational safety; path optimization; SURFACE VEHICLE;
D O I
10.3390/jmse11071439
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The influence of the maritime environment such as water currents, water depth, and traffic separation rules should be considered when conducting ship path planning. Additionally, the maneuverability constraints of the ship play a crucial role in navigation. Addressing the limitations of the traditional A-star algorithm in ship path planning, this paper proposes an improved A-star algorithm. Specifically, this paper examines the factors influencing ship navigation safety, and develops a risk model that takes into account water currents, water depth, and obstacles. The goal is to mitigate the total risk of ship collisions and grounding. Secondly, a traffic model is designed to ensure that the planned path adheres to the traffic separation rules and reduces the risk of collision with incoming ships. Then, a turning model and smoothing method are designed to make the generated path easy to track and control for the ship. To validate the effectiveness of the proposed A-star ship path-planning algorithm, three cases are studied in simulations and representative operational scenarios. The results of the cases demonstrate that the proposed A-star ship path-planning algorithm can better control the distance to obstacles, effectively avoid shallow water areas, and comply with traffic separation rules. The safety level of the path is effectively improved.
引用
收藏
页数:19
相关论文
共 34 条
  • [31] Improved Hybrid A-Star Algorithm for Path Planning in Autonomous Parking System Based on Multi-Stage Dynamic Optimization
    Meng, Tianchuang
    Yang, Tianhong
    Huang, Jin
    Jin, Wenrui
    Zhang, Wei
    Jia, Yifan
    Wan, Keqian
    Xiao, Gang
    Yang, Diange
    Zhong, Zhihua
    INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2023, 24 (02) : 459 - 468
  • [32] Improved A-Star Algorithm for Long-Distance Off-Road Path Planning Using Terrain Data Map
    Hong, Zhonghua
    Sun, Pengfei
    Tong, Xiaohua
    Pan, Haiyan
    Zhou, Ruyan
    Zhang, Yun
    Han, Yanling
    Wang, Jing
    Yang, Shuhu
    Xu, Lijun
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (11)
  • [33] Global Path Planning of UGVs in Large-Scale Off-Road Environment Based on Improved A-star Algorithm and Quadratic Programming
    Jiang, Junkai
    Han, Zeyu
    Li, Jinhao
    Wang, Yuning
    Wang, Jianqiang
    Xu, Shaobing
    2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV, 2023,
  • [34] Development of a New Intelligent Mobile Robot Path Planning Algorithm Based on Deep Reinforcement Learning Considering Pedestrian Traffic Rules
    Kubota, Koki
    Kobayashi, Kazuyuki
    Ohkubo, Tomoyuki
    Watanabe, Kajiro
    Sebi, Nashwan J.
    Tian, Kaiqiao
    Cheok, Ka C.
    2022 61ST ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS (SICE), 2022, : 628 - 632